Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and hands-free speech communication and teleconferencing systems; the position information can be fed into a higher processing stage for high-quality speech acquisition, enhancement of a specific speech signal in the presence of other competing talkers, or keeping a camera focused on the speaker in a video-conferencing scenario. Most of existing systems focus on the single source tracking problem, which assumes one and only one source is active all the time, and the state to be estimated is simply the source position. However, in practical scenarios, multiple speakers may be simultaneously active, and the tracking algorithm should be able to localise each individual source and estimate the number of sources. This thesis contains three contributions towards solutions to multiple acoustic source tracking in a moderate noisy and reverberant environment. The first contribution of this thesis is proposing a time-delay of arrival (TDOA) estimation approach for multiple sources. Although the phase transform (PHAT) weighted generalised cross-correlation (GCC) method has been employed to extract the TDOAs of multiple sources, it is primarily used for a single source scenario and its performance for multiple TDOA estimation has not been comprehensively studied. The proposed approach combines the degenerate unmixing estimation technique (DUET) and GCC method. Since the speech mixtures are assumed window-disjoint orthogonal (WDO) in the time-frequency domain, the spectrograms can be separated by employing DUET, and the GCC method can then be applied to the spectrogram of each individual source. The probabilities of detection and false alarm are also proposed to evaluate the TDOA estimation performance under a series of experimental parameters. Next, considering multiple acoustic sources may appear nonconcurrently, an extended Kalman particle filtering (EKPF) is developed for a special multiple acoustic source tracking problem, namely “nonconcurrent multiple acoustic tracking (NMAT)”. The extended Kalman filter (EKF) is used to approximate the optimum weights, and the subsequent particle filtering (PF) naturally takes the previous position estimates as well as the current TDOA measurements into account. The proposed approach is thus able to lock on the sharp change of the source position quickly, and avoid the tracking-lag in the general sequential importance resampling (SIR) PF. Finally, these investigations are extended into an approach to track the multiple unknown and time-varying number of acoustic sources. The DUET-GCC method is used to obtain the TDOA measurements for multiple sources and a random finite set (RFS) based Rao-blackwellised PF is employed and modified to track the sources. Each particle has a RFS form encapsulating the states of all sources and is capable of addressing source dynamics: source survival, new source appearance and source deactivation. A data association variable is defined to depict the source dynamic and its relation to the measurements. The Rao-blackwellisation step is used to decompose the state: the source positions are marginalised by using an EKF, and only the data association variable needs to be handled by a PF. The performances of all the proposed approaches are extensively studied under different noisy and reverberant environments, and are favorably comparable with the existing tracking techniques.

Robust object tracking plays a central role in many applications of image processing, computer vision and automatic control. In this thesis, robust object tracking under complex environments, including heavy clutters in the background, low resolution of the image sequences and non-stationary camera, has been studied. The interest of this study stems from the improvement of the performance of visual tracking using particle filtering. A Geometric Active contour-based Tracking Estimator, namely GATE, has been developed in order to tackle the problems in robust object tracking where the existence of multiple features or good object detection is not guaranteed. GATE combines particle filtering and the level set-based active contour method. The particle filtering method is able to deal with nonlinear and non-Gaussian recursive estimation problems, and the level set-based active contour method is capable of classifying state space of particle filtering under the methodology of one class classification. By integrating this classifier into the particle filtering, geometric information introduced by the shape prior and pose invariance of the tracked object in the level set-based active contour method can be utilised to prevent the particles corresponding to outlier measurements from being heavily reweighted. Hence, this procedure reshapes and refines the posterior distribution of the particle filtering. To verify the performance of GATE, the performance of the standard particle filter is compared with that of GATE. Since video sequences in different applications are usually captured by diverse devices, GATE and the standard particle filters with the identical initialisation are studied on image sequences captured by the handhold, stationary and PTZ camera, respectively. According to experimental results, even though a simple color observation model based on the Hue-Saturation-Value (HSV) color histogram is adopted, the newly developed. GATE significantly improves the performance of the particle filtering for object tracking in complex environments. Meanwhile, GATE initialises a novel approach to tackle the impoverishment problem for recursive Bayesian estimation using sampling method.

This work presents improvements to Monte Carlo Localization (MCL) for a mobile robot using computer vision. Solutions to the localization problem aim to provide fine resolution on location approximation, and also be resistant to changes in the environment. One such environment change is the kidnapped/teleported robot problem, where a robot is suddenly transported to a new location and must re-localize. The standard method of "Augmented MCL" uses particle filtering combined with addition of random particles under certain conditions to solve the kidnapped robot problem. This solution is robust, but not always fast. This work combines Histogram of Oriented Gradients (HOG) computer vision with particle filtering to speed up the localization process. The major slowdown in Augmented MCL is the conditional addition of random particles, which depends on the ratio of a short term and long term average of particle weights. This ratio does not change quickly when a robot is kidnapped, leading the robot to believe it is in the wrong location for a period of time. This work replaces this average-based conditional with a comparison of the HOG image directly in front of the robot with a cached version. This resulted in a speedup ranging from from 25.3% to 80.7% (depending on parameters used) in localization time over the baseline Augmented MCL.

Vehicle location and tracking has a variety of commercial applications and none of the techniques currently used can provide accurate results in all situations. This thesis details a preliminary investigation into a new location estimation method which uses optical environmental data, gathered by the vehicle during motion, to locate and track vehicle positions by comparing said data to pre-recorded optical maps of the intended location space. The design and implementation of an optical data recorder is presented. The map creation process is detailed and the location algorithm, based on a particle filter, is described in full. System tests were performed offline on a desktop PC using real world data collected by the data recorder and their results are presented. These tests show good performance for the system tracking the vehicle once its approximate location is determined. However locating a vehicle from scratch appears to be infeasible in a realistically large location space.

This thesis work analyses the obstacles faced when adapting the particle filtering algorithm to run on massively parallel compute architectures. Graphics processing units are one example of massively parallel compute architectures which allow for the developer to distribute computational load over hundreds or thousands of processor cores. This thesis studies an implementation written for NVIDIA GeForce GPUs, yielding varying speed ups, up to 3000% in some cases, when compared to the equivalent algorithm performed on CPU. The particle filter, also known in the literature as sequential Monte-Carlo methods, is an algorithm used for signal processing when the system generating the signals has a highly nonlinear behaviour or non-Gaussian noise distributions where a Kalman filter and its extended variants are not effective. The particle filter was chosen as a good candidate for parallelisation because of its inherently parallel nature. There are, however, several steps of the classic formulation where computations are dependent on other computations in the same step which requires them to be run in sequence instead of in parallel. To avoid these difficulties alternative ways of computing the results must be used, such as parallel scan operations and scatter/gather methods. Another area where parallel programming still is not widespread is the area of pseudo-random number generation. Pseudo-random numbers are required by the algorithm to simulate the process noise as well as for avoiding the particle depletion problem using a resampling step. In this thesis a recently published counter-based pseudo-random number generator is used.

This thesis deals with the problem of tracking highly deformable objects in the presence of noise, clutter and occlusions. The contributions of this thesis are threefold: A novel technique is proposed to perform filtering on an infinite dimensional space of curves for the purpose of tracking deforming objects. The algorithm combines the advantages of particle filter and geometric active contours to track deformable objects in the presence of noise and clutter. Shape information is quite useful in tracking deformable objects, especially if the objects under consideration get partially occluded. A nonlinear technique to perform shape analysis, called kernelized locally linear embedding, is proposed. Furthermore, a new algebraic method is proposed to compute the pre-image of the projection in the context of kernel PCA. This is further utilized in a parametric method to perform segmentation of medical images in the kernel PCA basis. The above mentioned shape learning methods are then incorporated into a generalized tracking algorithm to provide dynamic shape prior for tracking highly deformable objects. The tracker can also model image information like intensity moments or the output of a feature detector and can handle vector-valued (color) images.

Indoor localization with proximity information in ultra-high-frequency (UHF) radio-frequency-identification (RFID) is widely considered as a potential candidate of locating items in Internet-of-Things (IoT) paradigm. First, the proximity-based methods are less affected by multi-path distortion and dynamic changes of the indoor environment compared to the traditional range-based localization methods. The objective of this dissertation is to use tag-to-tag backscattering communication link in augmented UHF RFID system (AURIS) for proximity-based indoor localization solution. Tag-to-tag backscattering communication in AURIS has an obvious advantage over the conventional reader-to-tag link for proximity-based indoor localization by keeping both landmark and mobile tags simple and inexpensive. This work is the very first thesis evaluating proximity-based localization solution using tag-to-tag backscattering communication.Our research makes the contributions in terms of phase cancellation effect, the improved mathematical models and localization algorithm. First, we investigate the phase cancellation effect in the tag-to-tag backscattering communication, which has a significant effect on proximity-based localization. We then present a solution to counter such destructive effect by exploiting the spatial diversity of dual antennas. Second, a novel and realistic detection probability model of ST-to-tag detection is proposed. In AURIS, a large set of passive tags are placed at known locations as landmarks, and STs are attached mobile targets of interest. We identify two technical roadblocks of AURIS and existing localization algorithms as false synchronous detection assumption and state evolution model constraints. With the new and more realistic detection probability model we explore the use of particle filtering methodology for localizing ST, which overcomes the aforementioned roadblocks. Last, we propose a landmark-based sequential localization and mapping framework (SQLAM) for AURIS to locate STs and passive tags with unknown locations, which leverages a set of passive landmark tags to localize ST, and sequentially constructs a geographical map of passive tags with unknown locations while ST is moving in the environment. Mapping passive tags with unknown locations accurately leads to practical advantages. First, the localization capability of AURIS is not confined to the objects carrying STs. Second, the problem of failed landmark tags is addressed by including passive tags with resolved locations into landmark set. Each of the contributions is supported by extensive computer simulation to demonstrate the performance of enhancements.

More often than not, we cannot directly measure many phenomena that are crucial to us. However, we usually have access to certain partial observations on the phenomena of interest as well as a mathematical model of them. The filtering problem seeks estimation of the phenomena given all the accumulated partial information. In this thesis, we study several topics concerning the numerical approximation of the filtering problem. First, we study the continuous-time filtering problem. Given high-frequency ob- servations in discrete-time, we perform double discretization of the non-linear filter to allow for filter estimation with particle filter. By using the multilevel strategy, given any ε > 0, our algorithm achieve an MSE level of O(ε2) with a cost of O(ε−3), while the particle filter requires a cost of O(ε−4). Second, we propose a de-bias scheme for the particle filter under the partially observed diffusion model. The novel scheme is free of innate particle filter bias and discretization bias, through a double randomization method of [14]. Our estimator is perfectly parallel and achieves a similar cost reduction to the multilevel particle filter. Third, we look at a high-dimensional linear Gaussian state-space model in con- tinuous time. We propose a novel multilevel estimator which requires a cost of O(ε−2 log(ε)2) compared to ensemble Kalman-Bucy filters (EnKBFs) which requiresO(ε−3) for an MSE target of O(ε2). Simulation results verify our theory for models of di- mension ∼ 106. Lastly, we consider the model estimation through learning an unknown parameter that characterizes the partially observed diffusions. We propose algorithms to provide unbiased estimates of the Hessian and the inverse Hessian, which allows second-order optimization parameter learning for the model.

Estimating the state of a system from noisy measurements is a problem which arises in a variety of scientific and industrial areas which include signal processing, communications, statistics and econometrics. Recursive filtering is one way to achieve this by incorporating noisy observations as they become available with prior knowledge of the system model. Bayesian methods provide a general framework for dynamic state estimation problems. The central idea behind this recursive Bayesian estimation is computing the probability density function of the state vector of the system conditioned on the measurements. However, the optimal solution to this problem is often intractable because it requires high-dimensional integration. Although we can use the Kalman lter in the case of a linear state space model with Gaussian noise, this method is not optimum for a non-linear and non-Gaussian system model. There are many new methods of filtering for the general case. The main emphasis of this thesis is on one such recently developed filter, the particle lter [2,3,6]. In this thesis, a detailed introduction to particle filters is provided as well as some guidelines for the efficient implementation of the particle lter. The application of particle lters to various communication channels like detection of symbols over the channels, capacity calculation of the channel are discussed.

In this thesis we study Particle Filter for visual tracking applications. The sequential Monte Carlo methods or Particle Filter provides approximate solutions when the tracking problem involves non-linear and/or non-Gaussian state space models. Also in this study, in order to make the visual tracker robust against change in target appearance and unexpected target motion, an adaptive target appearance model and a first order motion estimator are embedded in particle filtering. Additionally, since pixels that don&rsquo / t belong to target makes the motion estimation biased, the algorithm includes robust estimators to make the tracker reliable. Within the scope of this thesis the visual tracker proposed in [5] is implemented and the same problem is solved by proposing a Rao-Blackwellized Particle Filter. To deal with problems encountered during the implementation phase of the algorithm some improvements are made such as utilizing learning rate for the computation of adaptive velocity estimation. Moreover, some precautions are taken such as checking the velocity estimations to validate them. Finally, we have done several experiments both in indoor and outdoor environments to demonstrate the effectiveness and robustness of the implemented algorithm. Experimental results show that most of the time the visual tracker performs well. On the other hand the drawbacks of the implemented tracker are indicated and we explain how to eliminate them.